import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from typing import Tuple
from .utils.logger import setup_logger

logger = setup_logger("DataPreprocessor")

class DataPreprocessor:
    def __init__(self, window_size: int = 100):
        self.window_size = window_size
        self.scaler = StandardScaler()

    def _sliding_window(self, df: pd.DataFrame) -> pd.DataFrame:
        """实现循环滑动窗口特征计算"""
        # 实现细节根据具体数据结构调整
        pass

    def preprocess(self, raw_data_path: str) -> Tuple[np.ndarray, pd.DataFrame]:
        """完整数据处理流水线"""
        logger.info(f"Loading raw data from {raw_data_path}")
        try:
            df = pd.read_csv(raw_data_path)

            # 数据清洗
            df = df[df['status_code'] == 200]
            df = df.drop_duplicates()

            # 特征工程
            logger.info("Calculating sliding window features")
            processed_df = self._sliding_window(df)

            # 处理缺失值
            processed_df['x5'].fillna(-1, inplace=True)

            # 标准化
            logger.info("Performing feature scaling")
            scaled_data = self.scaler.fit_transform(
                processed_df[['x3', 'x4', 'x5', 'x6', 'x7']]
            )

            return scaled_data, processed_df

        except Exception as e:
            logger.error(f"Data processing failed: {str(e)}")
            raise